Skip to main content

Solving IIR system identification by a variant of particle swarm optimization

Abstract

A variant of particle swarm optimization (PSO) is represented to solve the infinitive impulse response (IIR) system identification problem. Called improved PSO (IPSO), it makes significant enhancement over PSO. To begin with, the population initialization step makes use of golden ratio to segment solution space so as to obtain high-quality solutions. It is followed by all particles using different inertia weights in velocity updating step, which is beneficial for preserving the balance between global search and local search. Subsequently, IPSO uses normal distribution to disturb the global best particle, which enhances its capacity of escaping from the local optimums. The above three operations cannot only guarantee high-quality solutions, strong global search capacity, and fast convergence rate, but also avoid low diversity, excessive local search, and premature stagnation. These properties of IPSO make it much better suited for IIR system identification problems. IPSO is applied on 12 examples. The experimental results amply demonstrate the capability of IPSO toward obtaining the best objective function values in all the cases. Compared with the other four PSO approaches, IPSO has stronger convergence and higher stability which clearly points out its desirable performance in search accuracy and identifying efficiency.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

References

  1. 1.

    Krusienski DJ, Jenkins WK (2003) Adaptive filtering via particle swarm optimization. In: Proceedings of the 37th Asilomar conference on signals, systems and computers, vol 1, pp 571–575. doi:10.1109/ACSSC.2003.1291975

  2. 2.

    Krusienski DJ, Jenkins WK (2004) Particle swarm optimization for adaptive IIR filter structure. IEEE Congr Evolut Comput CEC 1:965–970. doi:10.1109/CEC.2004.1330966

    Google Scholar 

  3. 3.

    Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Global Optim 39(3):459–471. doi:10.1007/s10898-007-9149-x

    MathSciNet  Article  MATH  Google Scholar 

  4. 4.

    Karaboga N (2009) A new design method based on artificial bee colony algorithm for digital IIR filters. J Frankl Inst 346(4):328–348. doi:10.1016/j.jfranklin.2008.11.003

    MathSciNet  Article  MATH  Google Scholar 

  5. 5.

    Luitel B, Venayagamoorthy GK (2010) Particle swarm optimization with quantum infusion for system identification. Eng Appl Artif Intell 23(5):635–649. doi:10.1016/j.engappai.2010.01.022

    Article  Google Scholar 

  6. 6.

    Luitel B, Venayagamoorthy GK (2009) A PSO with quantum infusion algorithm for training simultaneous recurrent neural networks. In: IEEE-INNS international joint conference on neural networks (IJCNN), pp 1923–1930. doi:10.1109/IJCNN.2009.5179082

  7. 7.

    Dai CH, Chen WR, Zhu YF (2010) Seeker optimization algorithm for digital IIR filter design. IEEE Trans Ind Electron 57(5):1710–1718. doi:10.1109/TIE.2009.2031194

    Article  Google Scholar 

  8. 8.

    Panda G, Pradhan PM, Majhi B (2011) IIR system identification using cat swarm optimization. Expert Syst Appl 38(10):12671–12683. doi:10.1016/j.eswa.2011.04.054

    Article  Google Scholar 

  9. 9.

    Chu SC, Tsai PW (2007) Computational intelligence based on the behavior of cats. Int J Innov Comput Inf Control 3(1):163–173

    Google Scholar 

  10. 10.

    Upadhyay P, Kar R, Mandal D, Ghoshal SP (2014) Craziness based particle swarm optimization algorithm for IIR system identification problem. AEU-Int J Electron Commun 68(5):369–378. doi:10.1016/j.aeue.2013.10.003

    Article  Google Scholar 

  11. 11.

    Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, pp 1942–1948. doi:10.1109/ICNN.1995.488968

  12. 12.

    Shi YH, Eberhart RC (1999) Empirical study of particle swarm optimization. In: Proceedings of the IEEE congress on evolutionary computation, pp 1945–1950. doi:10.1109/CEC.1999.785511

  13. 13.

    White SA (1975) An adaptive recursive digital filter. In: Proceedings of the 9th asilomar conference: circuits, systems, computers, pp 21–25

  14. 14.

    Shynk JJ (1989) Adaptive IIR filtering. IEEE Trans Acoust Speech Signal Process 6(2):4–21. doi:10.1109/53.29644

    Google Scholar 

  15. 15.

    Ng S, Leung S, Chung C, Luk A, Lau W (1996) The genetic search approach: a new learning algorithm for adaptive iir filtering. IEEE Trans Signal Process 13(6):38–46. doi:10.1109/79.543974

    Article  Google Scholar 

  16. 16.

    Abe M, Kawamata M (1998) Evolutionary digital filtering for IIR adaptive digital filters based on the cloning and mating reproduction. IEICE Trans Fundam Electron Commun Computer Sci E81-A(3):398–406

    Google Scholar 

  17. 17.

    Kalinli A, Karaboga N (2005) Artificial immune algorithm for IIR filter design. Eng Appl Artif Intell 18(8):919–929. doi:10.1016/j.engappai.2005.03.009

    Article  MATH  Google Scholar 

  18. 18.

    Proakis JG, Manolakis DG (2007) Digital signal processing: principles, algorithms and applications, 4th edn. Pearson Education, New Jersey

    Google Scholar 

  19. 19.

    Zhao JJ, Ji GH, Xia Y, Zhang XL (2015) Cavitary nodule segmentation in computed tomography images based on self-generating neural networks and particle swarm optimisation. Int J Bio-Inspired Comput 7(1):62–67. doi:10.1504/IJBIC.2015.067999

    Article  Google Scholar 

  20. 20.

    Wang Z, Qin L, Yang W (2015) A self-organising cooperative hunting by robotic swarm based on particle swarm optimisation localisation. Int J Bio-Inspired Comput 7(1):68–73. doi:10.1504/IJBIC.2015.068001

    Article  Google Scholar 

  21. 21.

    Grillo H, Peidro D, Alemany M, Mula J (2015) Application of particle swarm optimisation with backward calculation to solve a fuzzy multi–objective supply chain master planning model. Int J Bio-Inspired Comput 7(3):157–169. doi:10.1504/IJBIC.2015.069557

    Article  Google Scholar 

  22. 22.

    Wang GG, Gandomi AH, Yang XS, Alavi AH (2014) A novel improved accelerated particle swarm optimization algorithm for global numerical optimization. Eng Comput 31(7):1198–1220. doi:10.1108/EC-10-2012-0232

    Article  Google Scholar 

  23. 23.

    Lu Q, Han QL, Liu SR (2014) A finite-time particle swarm optimization algorithm for odor source localization. Inf Sci 277:111–140. doi:10.1016/j.ins.2014.02.010

    MathSciNet  Article  MATH  Google Scholar 

  24. 24.

    Wang SC, Yeh MF (2014) A modified particle swarm optimization for aggregate production planning. Expert Syst Appl 41(6):3069–3077. doi:10.1016/j.eswa.2013.10.038

    Article  Google Scholar 

  25. 25.

    Boubaker S, Djemai M, Manamanni N, M’Sahli F (2014) Active modes and switching instants identification for linear switched systems based on discrete particle swarm optimization. Appl Soft Comput 14:482–488. doi:10.1016/j.asoc.2013.09.009

    Article  Google Scholar 

  26. 26.

    Jamali S, Shaker V (2014) Defense against SYN flooding attacks: a particle swarm optimization approach. Comput Electr Eng 40(6):2013–2025. doi:10.1016/j.compeleceng.2014.05.012

    Article  Google Scholar 

  27. 27.

    Sianoa P, Citro C (2014) Designing fuzzy logic controllers for DC-DC converters using multi-objective particle swarm optimization. Electr Power Syst Res 112:74–83. doi:10.1016/j.epsr.2014.03.010

    Article  Google Scholar 

  28. 28.

    Mandal S, Ghoshal SP, Kar R, Mandal D (2012) Design of optimal linear phase FIR highpass filter using craziness based particle swarm optimization technique. J King Saud Univ-Comp Inf Sci 24:83–92. doi:10.1016/j.jksuci.2011.10.007

    Google Scholar 

  29. 29.

    Mandal S, Ghoshal SP, Kar R, Mandal D (2011) Optimal linear phase FIR band passfilter design using craziness based particle swarm optimization algorithm. J Shanghai Jiaotong Univ (Science) 16(6):696–703. doi:10.1007/s12204-011-1213-5

    Article  Google Scholar 

  30. 30.

    Mandal D, Ghoshal SP, Bhattacharjee AK (2010) Radiation pattern optimization for concentric circular antenna array with central element feeding using craziness based particle swarm optimization. Int J RF Microw Comput Aided Eng 20(5):577–586. doi:10.1002/mmce.20467

    Article  Google Scholar 

  31. 31.

    Gao LQ, Li RP, Zou DX (2011) A global particle swarm optimization algorithm. J Northeastern Univ (Natural Science) 32(11):1538–1541

    MATH  Google Scholar 

  32. 32.

    Mohammadi-Ivatloo B, Moradi-Dalvand M, Rabiee A (2013) Combined heat and power economic dispatch problem solution using particle swarm optimization with time varying acceleration coefficients. Electr Power Syst Res 95:9–18. doi:10.1016/j.epsr.2012.08.005

    Article  Google Scholar 

  33. 33.

    Chaturvedi KT, Pandit M, Srivastava L (2009) Particle swarm optimization with time varying acceleration coefficients for non-convex economic power dispatch. Electr Power Energy Syst 31(6):249–257. doi:10.1016/j.ijepes.2009.01.010

    Article  Google Scholar 

  34. 34.

    Amaya I, Correa R (2015) Finding resonant frequencies of microwave cavities through a modified harmony search algorithm. Int J Bio-Inspired Comput 7(5):285–295. doi:10.1504/IJBIC.2015.072258

    Article  Google Scholar 

  35. 35.

    Bilbao MN, Ser JD, Salcedo-Sanz S, Casanova-Mateo C (2015) On the application of multi-objective harmony search heuristics to the predictive deployment of firefighting aircrafts: a realistic case study. Int J Bio-Inspired Comput 7(5):270–284. doi:10.1504/IJBIC.2015.072257

    Article  Google Scholar 

  36. 36.

    Coletta LF, Hruschka ER, Acharya A, Ghosh J (2015) A differential evolution algorithm to optimise the combination of classifier and cluster ensembles. Int J Bio-Inspired Comput 7(2):111–124. doi:10.1504/IJBIC.2015.069288

    Article  Google Scholar 

  37. 37.

    Amirjanov A, Sobolev K (2015) Changing range genetic algorithm for multimodal function optimisation. Int J Bio-Inspired Comput 7(4):209–221. doi:10.1504/IJBIC.2015.071075

    Article  Google Scholar 

  38. 38.

    Wang GG, Deb S, Gandomi AH, Zhang ZJ, Alavi AH (2015) Chaotic cuckoo search. Soft Comput. doi:10.1007/s00500-015-1726-1

    Google Scholar 

  39. 39.

    Yang XS, Deb S, Karamangolu M, He XS (2012) Cuckoo search for business optimization applications. In: Proceedings of NCCCS2012, IEEE, pp 1–5. doi:10.1109/NCCCS.2012.6412973

  40. 40.

    Wang G-G, Gandomi AH, Zhao X, Chu HE (2016) Hybridizing harmony search algorithm with cuckoo search for global numerical optimization. Soft Comput 20(1):273–285. doi:10.1007/s00500-014-1502-7

    Article  Google Scholar 

  41. 41.

    Wang GG, Deb S, Coelho LdS (2015) Earthworm optimization algorithm: a bio-inspired metaheuristic algorithm for global optimization problems. Int J Bio-Inspired Comput (in press)

  42. 42.

    Wang GG, Deb S, Gao X-Z, Coelho LdS (2016) A new metaheuristic optimization algorithm motivated by elephant herding behavior. Int J Bio-Inspired Comput (in press)

  43. 43.

    Wang GG, Deb S, Coelho LdS (2015) Elephant herding optimization. Paper presented at the 2015 3rd international symposium on computational and business intelligence (ISCBI 2015), Bali, Indonesia, December 7–9

  44. 44.

    Feng YH, Wang GG, Deb S, Lu M, Zhao XJ (2015) Solving 0-1 knapsack problem by a novel binary monarch butterfly optimization. Neural Comput Appl. doi:10.1007/s00521-015-2135-1

    Google Scholar 

  45. 45.

    Wang GG, Zhao X, Deb S (2015) A novel monarch butterfly optimization with greedy strategy and self-adaptive crossover operator. Paper presented at the 2015 2nd intelligence conference on soft computing & machine intelligence (ISCMI 2015), Hong Kong, 23–24 Nov 2015

  46. 46.

    Yang XS, Deb S (2014) Fong S (2014) Bat algorithm is better than intermittent search strategy. J Multi-Valued Logic Soft Comput 22(3):223–237

    Google Scholar 

  47. 47.

    Xue F, Cai Y, Cao Y, Cui Z, Li F (2015) Optimal parameter settings for bat algorithm. Int J Bio-Inspired Comput 7(2):125–128. doi:10.1504/ijbic.2015.069304

    Article  Google Scholar 

  48. 48.

    Wang GG, Chu HCE, Mirjalili S (2016) Three-dimensional path planning for UCAV using an improved bat algorithm. Aerosp Sci Technol 49:231–238. doi:10.1016/j.ast.2015.11.040

    Article  Google Scholar 

  49. 49.

    Wang GG, Gandomi AH, Alavi AH (2014) An effective krill herd algorithm with migration operator in biogeography-based optimization. Appl Math Model 38(9–10):2454–2462. doi:10.1016/j.apm.2013.10.052

    MathSciNet  Article  Google Scholar 

  50. 50.

    Wang GG, Deb S, Gandomi AH, Alavi AH (2016) Opposition-based krill herd algorithm with Cauchy mutation and position clamping. Neurocomputing 177:147–157. doi:10.1016/j.neucom.2015.11.018

    Article  Google Scholar 

  51. 51.

    Wang GG, Gandomi AH, Alavi AH, Hao G-S (2014) Hybrid krill herd algorithm with differential evolution for global numerical optimization. Neural Comput Appl 25(2):297–308. doi:10.1007/s00521-013-1485-9

    Article  Google Scholar 

  52. 52.

    Wang GG, Gandomi AH, Alavi AH (2013) A chaotic particle-swarm krill herd algorithm for global numerical optimization. Kybernetes 42(6):962–978. doi:10.1108/K-11-2012-0108

    MathSciNet  Article  Google Scholar 

  53. 53.

    Henein MY, Collaborators GR, Zhao Y, Nicoll R, Sun L, Khir AW, Franklin K, Lindqvist P (2011) The human heart: application of the golden ratio and angle. Int J Cardiol 150(3):239–242. doi:10.1016/j.ijcard.2011.05.094

    Article  Google Scholar 

  54. 54.

    Xie ZF (2011) The golden ratio and super central configurations of the n-body problem. J Differ Equ 251(1):58–72. doi:10.1016/j.jde.2011.03.002

    MathSciNet  Article  MATH  Google Scholar 

  55. 55.

    Schmid K, Marx D, Samal A (2008) Computation of a face attractiveness index based on neoclassical canons, symmetry, and golden ratios. Pattern Recogn 41(8):2710–2717. doi:10.1016/j.patcog.2007.11.022

    Article  Google Scholar 

  56. 56.

    Pallett PM, Link S, Lee K (2010) New “golden” ratios for facial beauty. Vis Res 50(2):149–154. doi:10.1016/j.visres.2009.11.003

    Article  Google Scholar 

  57. 57.

    Majhi B, Panda G, Choubey A (2008) Efficient scheme of pole-zero system identification using particle swarm optimization technique. In: IEEE congress on evolutionary computation, pp 446–451. doi:10.1109/CEC.2008.4630836

  58. 58.

    Durmus B, Gun A (2011) Parameter identification using particle swarm optimization. In: 6th International advanced technologies symposium, pp 188–92

  59. 59.

    Yu X, Liu J, Li H (2009) An adaptive inertia weight particle swarm optimization algorithm for IIR digital filter. IEEE Int Conf Artif Comput Intell 1:114–118. doi:10.1109/AICI.2009.28

    Google Scholar 

  60. 60.

    Wang GG, Guo LH, Wang HQ, Duan H, Liu L, Li J (2014) Incorporating mutation scheme into krill herd algorithm for global numerical optimization. Neural Comput Appl 24(3):853–871. doi:10.1007/s00521-012-1304-8

    Article  Google Scholar 

  61. 61.

    Wang GG, Guo LH, Gandomi AH, Hao GS, Wang HQ (2014) Chaotic krill herd algorithm. Inf Sci 274:17–34. doi:10.1016/j.ins.2014.02.123

    MathSciNet  Article  Google Scholar 

  62. 62.

    Wang GG, Gandomi AH, Alavi AH (2014) Stud krill herd algorithm. Neurocomputing 128:363–370. doi:10.1016/j.neucom.2013.08.031

    Article  Google Scholar 

  63. 63.

    Guo LH, Wang GG, Gandomi AH, Alavi AH, Duan H (2014) A new improved krill herd algorithm for global numerical optimization. Neurocomputing 138:392–402. doi:10.1016/j.neucom.2014.01.023

    Article  Google Scholar 

  64. 64.

    Wang GG, Gandomi AH, Yang XS, Alavi AH (2014) A new hybrid method based on krill herd and cuckoo search for global optimization tasks. Int J Bio-Inspired Comput (in press)

  65. 65.

    Yang XS, Deb S, Fong S (2014) Metaheuristic algorithms: optimal balance of intensification & diversification. Appl Math Inf Sci 8(3):977–983. doi:10.12785/amis/080306

    Article  Google Scholar 

  66. 66.

    Yang XS, Deb S, Hanne T, He X (2015) Attraction and diffusion in nature-inspired optimization algorithms. Neural Comput Appl. doi:10.1007/s00521-015-1925-9

    Google Scholar 

  67. 67.

    Cuevas E, González A, Zaldívar D, Pérez-Cisneros M (2015) An optimisation algorithm based on the behaviour of locust swarms. Int J Bio-Inspired Comput 7(6):402–407. doi:10.1504/IJBIC.2015.073178

    Article  Google Scholar 

  68. 68.

    Guo L, Wang G-G, Wang H, Wang D (2013) An effective hybrid firefly algorithm with harmony search for global numerical optimization. Sci World J 2013:9, Article ID 125625. doi:10.1155/2013/125625

  69. 69.

    Duan H, Zhao W, Wang G-G, Feng X (2012) Test-sheet composition using analytic hierarchy process and hybrid metaheuristic algorithm TS/BBO. Math Probl Eng 2012:22, Article ID 712752. doi:10.1155/2012/712752

  70. 70.

    Wang G-G, Guo L, Duan H, Liu L, Wang H, Wang J (2012) A hybrid meta-heuristic DE/CS algorithm for UCAV path planning. J Inf Comput Sci 9(16):4811–4818

    Google Scholar 

Download references

Acknowledgments

This work was supported by the National Natural Science Foundation of China (Nos. 61403174, 61503165), Jiangsu Province Science Foundation for Youths (No. BK20150239).

Author information

Affiliations

Authors

Corresponding author

Correspondence to Gai-Ge Wang.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Zou, DX., Deb, S. & Wang, GG. Solving IIR system identification by a variant of particle swarm optimization. Neural Comput & Applic 30, 685–698 (2018). https://doi.org/10.1007/s00521-016-2338-0

Download citation

Keywords

  • Improved particle swarm optimization
  • IIR system identification
  • Golden ratio
  • Inertia weight
  • Normal distribution